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Full polarization SAR image classification using deep learning with shallow feature

机译：使用浅层特征的深度学习进行全极化SAR图像分类

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摘要

The classification of the POL-SAR image become more and more important with the development of the polarization of synthetic aperture radar system. Generally, the classification of POL-SAR images are based on polarization feature, such as support vector machine (SVM), Wishart clustering and other methods. Specifically, some ground objects usually have some weak scattering characteristics which cannot obtain good results by only using the traditional classification based on polarization features. So, the deep learning based on T matrix is used to mine the powerful feature of SAR data. In order to speed up computation and improve classification accuracy, a classification of full-polarization SAR images based on Deep Learning with Shallow features is proposed in this paper. The proposed method can get better classification for those weak scatter objects than those methods only using polarization features.
机译：随着合成孔径雷达系统极化的发展，POL-SAR图像的分类变得越来越重要。通常，POL-SAR图像的分类基于极化特征，例如支持向量机（SVM），Wishart聚类和其他方法。具体地，一些地面物体通常具有一些弱散射特性，仅通过使用基于偏振特征的传统分类就不能获得良好的结果。因此，基于T矩阵的深度学习被用于挖掘SAR数据的强大功能。为了加快计算速度，提高分类精度，提出了一种基于深度学习浅特征的全极化SAR图像分类方法。与仅使用偏振特征的方法相比，所提出的方法对于那些弱散射对象可以获得更好的分类。

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